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Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals

The electrocardiogram (ECG) is a useful diagnostic tool to diagnose various cardiovascular diseases (CVDs) such as myocardial infarction (MI). The ECG records the heart's electrical activity and these signals are able to reflect the abnormal activity of the heart. However, it is challenging to...

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Main Authors:	Acharya, U.R., Fujita, H., Oh, S.L., Hagiwara, Y., Tan, J.H., Adam, M.
Format:	Article
Published:	Elsevier 2017
Subjects:	TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://eprints.um.edu.my/17551/ https://doi.org/10.1016/j.ins.2017.06.027
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spelling	my.um.eprints.175512017-07-20T08:50:58Z http://eprints.um.edu.my/17551/ Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals Acharya, U.R. Fujita, H. Oh, S.L. Hagiwara, Y. Tan, J.H. Adam, M. TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering The electrocardiogram (ECG) is a useful diagnostic tool to diagnose various cardiovascular diseases (CVDs) such as myocardial infarction (MI). The ECG records the heart's electrical activity and these signals are able to reflect the abnormal activity of the heart. However, it is challenging to visually interpret the ECG signals due to its small amplitude and duration. Therefore, we propose a novel approach to automatically detect the MI using ECG signals. In this study, we implemented a convolutional neural network (CNN) algorithm for the automated detection of a normal and MI ECG beats (with noise and without noise). We achieved an average accuracy of 93.53% and 95.22% using ECG beats with noise and without noise removal respectively. Further, no feature extraction or selection is performed in this work. Hence, our proposed algorithm can accurately detect the unknown ECG signals even with noise. So, this system can be introduced in clinical settings to aid the clinicians in the diagnosis of MI. Elsevier 2017 Article PeerReviewed Acharya, U.R. and Fujita, H. and Oh, S.L. and Hagiwara, Y. and Tan, J.H. and Adam, M. (2017) Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Information Sciences, 415. pp. 190-198. ISSN 0020-0255 https://doi.org/10.1016/j.ins.2017.06.027 DOI: 10.1016/j.ins.2017.06.027
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Acharya, U.R. Fujita, H. Oh, S.L. Hagiwara, Y. Tan, J.H. Adam, M. Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals
description	The electrocardiogram (ECG) is a useful diagnostic tool to diagnose various cardiovascular diseases (CVDs) such as myocardial infarction (MI). The ECG records the heart's electrical activity and these signals are able to reflect the abnormal activity of the heart. However, it is challenging to visually interpret the ECG signals due to its small amplitude and duration. Therefore, we propose a novel approach to automatically detect the MI using ECG signals. In this study, we implemented a convolutional neural network (CNN) algorithm for the automated detection of a normal and MI ECG beats (with noise and without noise). We achieved an average accuracy of 93.53% and 95.22% using ECG beats with noise and without noise removal respectively. Further, no feature extraction or selection is performed in this work. Hence, our proposed algorithm can accurately detect the unknown ECG signals even with noise. So, this system can be introduced in clinical settings to aid the clinicians in the diagnosis of MI.
format	Article
author	Acharya, U.R. Fujita, H. Oh, S.L. Hagiwara, Y. Tan, J.H. Adam, M.
author_facet	Acharya, U.R. Fujita, H. Oh, S.L. Hagiwara, Y. Tan, J.H. Adam, M.
author_sort	Acharya, U.R.
title	Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals
title_short	Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals
title_full	Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals
title_fullStr	Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals
title_full_unstemmed	Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals
title_sort	application of deep convolutional neural network for automated detection of myocardial infarction using ecg signals
publisher	Elsevier
publishDate	2017
url	http://eprints.um.edu.my/17551/ https://doi.org/10.1016/j.ins.2017.06.027
_version_	1643690450256658432
score	13.211869

Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals

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